Artificial Intelligence Portfolio Risk and Returns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import warnings
warnings.filterwarnings("ignore")

# fix_yahoo_finance is used to fetch data 
import yfinance as yf
yf.pdr_override()

# input
# A.I. Stock
symbols = ['TCEHY','NVDA','MSFT','TWLO','AMZN','GOOGL','NFLX','CRM','DE','SPLK','VERI','SNPS','QUIK','BRN.AX']
start = '2016-01-01'
end = '2019-12-28'

df = pd.DataFrame()
for s in symbols:
    df[s] = yf.download(s,start,end)['Adj Close']

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from datetime import datetime
from dateutil import relativedelta

d1 = datetime.strptime(start, "%Y-%m-%d")
d2 = datetime.strptime(end, "%Y-%m-%d")
delta = relativedelta.relativedelta(d2,d1)
print('How many years of investing?')
print('%s years' % delta.years)

How many years of investing?
3 years

number_of_years = delta.years

days = (df.index[-1] - df.index[0]).days
days

1453

df.head()

	TCEHY	NVDA	MSFT	TWLO	AMZN	GOOGL	NFLX	CRM	DE	SPLK	VERI	SNPS	QUIK	BRN.AX
Date
2016-01-04	18.936167	31.724598	50.536495	NaN	636.989990	759.440002	109.959999	76.709999	70.244408	57.650002	NaN	44.919998	17.639999	0.24090
2016-01-05	18.886673	32.234219	50.767044	NaN	633.789978	761.530029	107.660004	77.050003	70.650658	57.200001	NaN	44.970001	19.320000	0.23627
2016-01-06	18.470930	30.901340	49.844841	NaN	632.650024	759.330017	117.680000	76.290001	69.856628	55.189999	NaN	44.310001	17.080000	0.23627
2016-01-07	17.728529	29.676266	48.111115	NaN	607.940002	741.000000	114.559998	74.300003	69.238022	52.980000	NaN	43.279999	17.080000	0.24554
2016-01-08	17.787920	29.039225	48.258663	NaN	607.049988	730.909973	111.389999	73.230003	68.102364	52.009998	NaN	42.349998	16.799999	0.23164

df.tail()

	TCEHY	NVDA	MSFT	TWLO	AMZN	GOOGL	NFLX	CRM	DE	SPLK	VERI	SNPS	QUIK	BRN.AX
Date
2019-12-20	48.150002	239.369995	157.410004	100.260002	1786.500000	1351.219971	336.899994	164.550003	174.649994	150.960007	2.61	139.880005	4.62	0.0420
2019-12-23	48.459999	238.820007	157.410004	100.389999	1793.000000	1350.630005	333.100006	163.740005	175.050003	150.050003	2.60	139.910004	4.20	0.0425
2019-12-24	48.340000	238.619995	157.380005	100.550003	1789.209961	1344.430054	333.200012	163.250000	174.500000	150.350006	2.64	138.210007	4.06	0.0420
2019-12-26	48.810001	239.190002	158.669998	102.610001	1868.770020	1362.469971	332.630005	164.509995	174.800003	151.919998	2.52	140.070007	4.43	NaN
2019-12-27	49.240002	236.869995	158.960007	100.430000	1869.800049	1354.640015	329.089996	164.979996	175.809998	151.509995	2.55	140.619995	4.58	0.0440

plt.figure(figsize=(12,8))
plt.plot(df)
plt.title('Artificial Intelligence Stocks Closing Price')
plt.legend(labels=df.columns)

<matplotlib.legend.Legend at 0x15e4d602c18>

# Normalize the data
normalize = (df - df.min())/ (df.max() - df.min())

plt.figure(figsize=(18,12))
plt.plot(normalize)
plt.title('Artificial Intelligence Stocks Normalize')
plt.legend(labels=normalize.columns)

<matplotlib.legend.Legend at 0x15e4e87a048>

stock_rets = df.pct_change().dropna()

plt.figure(figsize=(12,8))
plt.plot(stock_rets)
plt.title('Artificial Intelligence Stocks Returns')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x15e4e68cf28>

plt.figure(figsize=(12,8))
plt.plot(stock_rets.cumsum())
plt.title('Artificial Intelligence Stocks Returns Cumulative Sum')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x15e4e703d30>

sns.set(style='ticks')
ax = sns.pairplot(stock_rets, diag_kind='hist')

nplot = len(stock_rets.columns)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

ax = sns.PairGrid(stock_rets)
ax.map_upper(plt.scatter, color='purple')
ax.map_lower(sns.kdeplot, color='blue')
ax.map_diag(plt.hist, bins=30)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

plt.figure(figsize=(7,7))
corr = stock_rets.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns,
            cmap="Blues")

<matplotlib.axes._subplots.AxesSubplot at 0x15e5e455ba8>

# Box plot
stock_rets.plot(kind='box',figsize=(12,8))

<matplotlib.axes._subplots.AxesSubplot at 0x15e56761978>

rets = stock_rets.dropna()

plt.figure(figsize=(12,8))
plt.scatter(rets.mean(), rets.std(),alpha = 0.5)

plt.title('Stocks Risk & Returns')
plt.xlabel('Expected returns')
plt.ylabel('Risk')
plt.grid(which='major')

for label, x, y in zip(rets.columns, rets.mean(), rets.std()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

rets = stock_rets.dropna()
area = np.pi*20.0

sns.set(style='darkgrid')
plt.figure(figsize=(12,8))
plt.scatter(rets.mean(), rets.std(), s=area)
plt.xlabel("Expected Return", fontsize=15)
plt.ylabel("Risk", fontsize=15)
plt.title("Return vs. Risk for Stocks", fontsize=20)

for label, x, y in zip(rets.columns, rets.mean(), rets.std()) : 
    plt.annotate(label, xy=(x,y), xytext=(50, 0), textcoords='offset points',
                arrowprops=dict(arrowstyle='-', connectionstyle='bar,angle=180,fraction=-0.2'),
                bbox=dict(boxstyle="round", fc="w"))

rest_rets = rets.corr()
pair_value = rest_rets.abs().unstack()
pair_value.sort_values(ascending = False)

BRN.AX  BRN.AX    1.000000
QUIK    QUIK      1.000000
NVDA    NVDA      1.000000
MSFT    MSFT      1.000000
TWLO    TWLO      1.000000
AMZN    AMZN      1.000000
GOOGL   GOOGL     1.000000
NFLX    NFLX      1.000000
CRM     CRM       1.000000
DE      DE        1.000000
SPLK    SPLK      1.000000
VERI    VERI      1.000000
SNPS    SNPS      1.000000
TCEHY   TCEHY     1.000000
AMZN    MSFT      0.749717
MSFT    AMZN      0.749717
        GOOGL     0.741069
GOOGL   MSFT      0.741069
MSFT    CRM       0.723773
CRM     MSFT      0.723773
AMZN    GOOGL     0.693280
GOOGL   AMZN      0.693280
MSFT    SNPS      0.680529
SNPS    MSFT      0.680529
        CRM       0.659417
CRM     SNPS      0.659417
AMZN    CRM       0.640889
CRM     AMZN      0.640889
SPLK    CRM       0.635718
CRM     SPLK      0.635718
                    ...   
QUIK    NVDA      0.066894
NVDA    QUIK      0.066894
QUIK    SNPS      0.063031
SNPS    QUIK      0.063031
QUIK    VERI      0.057122
VERI    QUIK      0.057122
QUIK    BRN.AX    0.054093
BRN.AX  QUIK      0.054093
SNPS    BRN.AX    0.049678
BRN.AX  SNPS      0.049678
        VERI      0.037270
VERI    BRN.AX    0.037270
QUIK    DE        0.036152
DE      QUIK      0.036152
BRN.AX  AMZN      0.027664
AMZN    BRN.AX    0.027664
MSFT    BRN.AX    0.027661
BRN.AX  MSFT      0.027661
        NVDA      0.027138
NVDA    BRN.AX    0.027138
SPLK    BRN.AX    0.014774
BRN.AX  SPLK      0.014774
        TWLO      0.010263
TWLO    BRN.AX    0.010263
BRN.AX  TCEHY     0.009593
TCEHY   BRN.AX    0.009593
BRN.AX  GOOGL     0.009283
GOOGL   BRN.AX    0.009283
BRN.AX  CRM       0.005423
CRM     BRN.AX    0.005423
Length: 196, dtype: float64

# Normalized Returns Data
Normalized_Value = ((rets[:] - rets[:].min()) /(rets[:].max() - rets[:].min()))
Normalized_Value.head()

	TCEHY	NVDA	MSFT	TWLO	AMZN	GOOGL	NFLX	CRM	DE	SPLK	VERI	SNPS	QUIK	BRN.AX
Date
2017-05-15	0.604207	0.845541	0.423468	0.305080	0.355014	0.463089	0.410687	0.503740	0.552379	0.431741	0.213241	0.514984	0.371527	0.288117
2017-05-16	0.543056	0.733211	0.572682	0.328186	0.411922	0.470960	0.415309	0.455829	0.570345	0.411476	0.397156	0.470888	0.398390	0.331400
2017-05-17	0.541203	0.430727	0.204026	0.273956	0.266867	0.302275	0.267741	0.346623	0.420149	0.252423	0.514054	0.255375	0.450704	0.375793
2017-05-18	0.673671	0.815984	0.444054	0.243629	0.440813	0.489778	0.499835	0.453364	0.478520	0.339598	0.499247	0.162781	0.450704	0.288117
2017-05-19	0.680667	0.749147	0.415574	0.345561	0.378423	0.463642	0.466913	0.437746	1.000000	0.417436	0.470014	0.459793	0.483255	0.331400

Normalized_Value.corr()

	TCEHY	NVDA	MSFT	TWLO	AMZN	GOOGL	NFLX	CRM	DE	SPLK	VERI	SNPS	QUIK	BRN.AX
TCEHY	1.000000	0.461313	0.495002	0.327239	0.480675	0.486139	0.423656	0.460030	0.426640	0.382437	0.158167	0.423648	0.085437	-0.009593
NVDA	0.461313	1.000000	0.582348	0.406732	0.579325	0.566149	0.492555	0.545321	0.448220	0.434418	0.138721	0.575646	0.066894	0.027138
MSFT	0.495002	0.582348	1.000000	0.533268	0.749717	0.741069	0.582075	0.723773	0.466336	0.501719	0.144108	0.680529	0.102503	-0.027661
TWLO	0.327239	0.406732	0.533268	1.000000	0.512671	0.473900	0.426523	0.609668	0.303066	0.505737	0.127248	0.550771	0.108106	-0.010263
AMZN	0.480675	0.579325	0.749717	0.512671	1.000000	0.693280	0.631191	0.640889	0.430530	0.473525	0.152899	0.582122	0.118188	0.027664
GOOGL	0.486139	0.566149	0.741069	0.473900	0.693280	1.000000	0.567633	0.616621	0.457178	0.447449	0.165993	0.585296	0.098187	0.009283
NFLX	0.423656	0.492555	0.582075	0.426523	0.631191	0.567633	1.000000	0.579740	0.341174	0.441888	0.181313	0.488839	0.117592	0.075573
CRM	0.460030	0.545321	0.723773	0.609668	0.640889	0.616621	0.579740	1.000000	0.391727	0.635718	0.138729	0.659417	0.094335	-0.005423
DE	0.426640	0.448220	0.466336	0.303066	0.430530	0.457178	0.341174	0.391727	1.000000	0.286567	0.152714	0.371667	0.036152	0.067035
SPLK	0.382437	0.434418	0.501719	0.505737	0.473525	0.447449	0.441888	0.635718	0.286567	1.000000	0.143642	0.499238	0.126354	-0.014774
VERI	0.158167	0.138721	0.144108	0.127248	0.152899	0.165993	0.181313	0.138729	0.152714	0.143642	1.000000	0.141900	0.057122	0.037270
SNPS	0.423648	0.575646	0.680529	0.550771	0.582122	0.585296	0.488839	0.659417	0.371667	0.499238	0.141900	1.000000	0.063031	-0.049678
QUIK	0.085437	0.066894	0.102503	0.108106	0.118188	0.098187	0.117592	0.094335	0.036152	0.126354	0.057122	0.063031	1.000000	0.054093
BRN.AX	-0.009593	0.027138	-0.027661	-0.010263	0.027664	0.009283	0.075573	-0.005423	0.067035	-0.014774	0.037270	-0.049678	0.054093	1.000000

normalized_rets = Normalized_Value.corr()
normalized_pair_value = normalized_rets.abs().unstack()
normalized_pair_value.sort_values(ascending = False)

BRN.AX  BRN.AX    1.000000
QUIK    QUIK      1.000000
NVDA    NVDA      1.000000
MSFT    MSFT      1.000000
TWLO    TWLO      1.000000
AMZN    AMZN      1.000000
GOOGL   GOOGL     1.000000
NFLX    NFLX      1.000000
CRM     CRM       1.000000
DE      DE        1.000000
SPLK    SPLK      1.000000
VERI    VERI      1.000000
SNPS    SNPS      1.000000
TCEHY   TCEHY     1.000000
AMZN    MSFT      0.749717
MSFT    AMZN      0.749717
        GOOGL     0.741069
GOOGL   MSFT      0.741069
MSFT    CRM       0.723773
CRM     MSFT      0.723773
AMZN    GOOGL     0.693280
GOOGL   AMZN      0.693280
MSFT    SNPS      0.680529
SNPS    MSFT      0.680529
        CRM       0.659417
CRM     SNPS      0.659417
AMZN    CRM       0.640889
CRM     AMZN      0.640889
SPLK    CRM       0.635718
CRM     SPLK      0.635718
                    ...   
QUIK    NVDA      0.066894
NVDA    QUIK      0.066894
QUIK    SNPS      0.063031
SNPS    QUIK      0.063031
QUIK    VERI      0.057122
VERI    QUIK      0.057122
QUIK    BRN.AX    0.054093
BRN.AX  QUIK      0.054093
SNPS    BRN.AX    0.049678
BRN.AX  SNPS      0.049678
        VERI      0.037270
VERI    BRN.AX    0.037270
QUIK    DE        0.036152
DE      QUIK      0.036152
BRN.AX  AMZN      0.027664
AMZN    BRN.AX    0.027664
MSFT    BRN.AX    0.027661
BRN.AX  MSFT      0.027661
        NVDA      0.027138
NVDA    BRN.AX    0.027138
SPLK    BRN.AX    0.014774
BRN.AX  SPLK      0.014774
        TWLO      0.010263
TWLO    BRN.AX    0.010263
BRN.AX  TCEHY     0.009593
TCEHY   BRN.AX    0.009593
BRN.AX  GOOGL     0.009283
GOOGL   BRN.AX    0.009283
BRN.AX  CRM       0.005423
CRM     BRN.AX    0.005423
Length: 196, dtype: float64

print("Stock returns: ")
print(rets.mean())
print('-' * 50)
print("Stock risks:")
print(rets.std())

Stock returns: 
TCEHY     0.000811
NVDA      0.001325
MSFT      0.001452
TWLO      0.002733
AMZN      0.001170
GOOGL     0.000644
NFLX      0.001380
CRM       0.001084
DE        0.000880
SPLK      0.001542
VERI     -0.000402
SNPS      0.001067
QUIK     -0.000995
BRN.AX   -0.000880
dtype: float64
--------------------------------------------------
Stock risks:
TCEHY     0.020757
NVDA      0.027282
MSFT      0.014360
TWLO      0.034306
AMZN      0.018133
GOOGL     0.015159
NFLX      0.024406
CRM       0.017334
DE        0.016866
SPLK      0.025274
VERI      0.065553
SNPS      0.014377
QUIK      0.051383
BRN.AX    0.051811
dtype: float64

table = pd.DataFrame()
table['Returns'] = rets.mean()
table['Risk'] = rets.std()
table.sort_values(by='Returns')

	Returns	Risk
QUIK	-0.000995	0.051383
BRN.AX	-0.000880	0.051811
VERI	-0.000402	0.065553
GOOGL	0.000644	0.015159
TCEHY	0.000811	0.020757
DE	0.000880	0.016866
SNPS	0.001067	0.014377
CRM	0.001084	0.017334
AMZN	0.001170	0.018133
NVDA	0.001325	0.027282
NFLX	0.001380	0.024406
MSFT	0.001452	0.014360
SPLK	0.001542	0.025274
TWLO	0.002733	0.034306

table.sort_values(by='Risk')

	Returns	Risk
MSFT	0.001452	0.014360
SNPS	0.001067	0.014377
GOOGL	0.000644	0.015159
DE	0.000880	0.016866
CRM	0.001084	0.017334
AMZN	0.001170	0.018133
TCEHY	0.000811	0.020757
NFLX	0.001380	0.024406
SPLK	0.001542	0.025274
NVDA	0.001325	0.027282
TWLO	0.002733	0.034306
QUIK	-0.000995	0.051383
BRN.AX	-0.000880	0.051811
VERI	-0.000402	0.065553

rf = 0.01
table['Sharpe Ratio'] = (table['Returns'] - rf) / table['Risk']
table

	Returns	Risk	Sharpe Ratio
TCEHY	0.000811	0.020757	-0.442704
NVDA	0.001325	0.027282	-0.317969
MSFT	0.001452	0.014360	-0.595245
TWLO	0.002733	0.034306	-0.211827
AMZN	0.001170	0.018133	-0.486935
GOOGL	0.000644	0.015159	-0.617195
NFLX	0.001380	0.024406	-0.353175
CRM	0.001084	0.017334	-0.514396
DE	0.000880	0.016866	-0.540723
SPLK	0.001542	0.025274	-0.334653
VERI	-0.000402	0.065553	-0.158685
SNPS	0.001067	0.014377	-0.621371
QUIK	-0.000995	0.051383	-0.213989
BRN.AX	-0.000880	0.051811	-0.210003

table['Max Returns'] = rets.max()

table['Min Returns'] = rets.min()

table['Median Returns'] = rets.median()

total_return = stock_rets[-1:].transpose()
table['Total Return'] = 100 * total_return
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return
TCEHY	0.000811	0.020757	-0.442704	0.083018	-0.107100	0.000786	0.880968
NVDA	0.001325	0.027282	-0.317969	0.093632	-0.187559	0.002067	-0.969943
MSFT	0.001452	0.014360	-0.595245	0.075705	-0.054338	0.001475	0.182775
TWLO	0.002733	0.034306	-0.211827	0.354407	-0.137602	0.002354	-2.124550
AMZN	0.001170	0.018133	-0.486935	0.132164	-0.078197	0.001425	0.055118
GOOGL	0.000644	0.015159	-0.617195	0.096202	-0.075019	0.001013	-0.574688
NFLX	0.001380	0.024406	-0.353175	0.135436	-0.102720	0.000590	-1.064248
CRM	0.001084	0.017334	-0.514396	0.102713	-0.087061	0.001696	0.285698
DE	0.000880	0.016866	-0.540723	0.073045	-0.076512	0.001518	0.577800
SPLK	0.001542	0.025274	-0.334653	0.178932	-0.111918	0.001420	-0.269881
VERI	-0.000402	0.065553	-0.158685	0.493011	-0.302837	-0.005348	1.190475
SNPS	0.001067	0.014377	-0.621371	0.063589	-0.052558	0.001783	0.392652
QUIK	-0.000995	0.051383	-0.213989	0.375000	-0.307692	0.000000	3.386007
BRN.AX	-0.000880	0.051811	-0.210003	0.386330	-0.191489	0.000000	4.761906

table['Average Return Yearly'] = (1 + total_return)**(1 / number_of_years) - 1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Yearly
TCEHY	0.000811	0.020757	-0.442704	0.083018	-0.107100	0.000786	0.880968	0.002928
NVDA	0.001325	0.027282	-0.317969	0.093632	-0.187559	0.002067	-0.969943	-0.003244
MSFT	0.001452	0.014360	-0.595245	0.075705	-0.054338	0.001475	0.182775	0.000609
TWLO	0.002733	0.034306	-0.211827	0.354407	-0.137602	0.002354	-2.124550	-0.007133
AMZN	0.001170	0.018133	-0.486935	0.132164	-0.078197	0.001425	0.055118	0.000184
GOOGL	0.000644	0.015159	-0.617195	0.096202	-0.075019	0.001013	-0.574688	-0.001919
NFLX	0.001380	0.024406	-0.353175	0.135436	-0.102720	0.000590	-1.064248	-0.003560
CRM	0.001084	0.017334	-0.514396	0.102713	-0.087061	0.001696	0.285698	0.000951
DE	0.000880	0.016866	-0.540723	0.073045	-0.076512	0.001518	0.577800	0.001922
SPLK	0.001542	0.025274	-0.334653	0.178932	-0.111918	0.001420	-0.269881	-0.000900
VERI	-0.000402	0.065553	-0.158685	0.493011	-0.302837	-0.005348	1.190475	0.003953
SNPS	0.001067	0.014377	-0.621371	0.063589	-0.052558	0.001783	0.392652	0.001307
QUIK	-0.000995	0.051383	-0.213989	0.375000	-0.307692	0.000000	3.386007	0.011162
BRN.AX	-0.000880	0.051811	-0.210003	0.386330	-0.191489	0.000000	4.761906	0.015628

initial_value = df.iloc[0]
ending_value = df.iloc[-1]
table['CAGR'] = ((ending_value / initial_value) ** (252.0 / days)) -1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Yearly	CAGR
TCEHY	0.000811	0.020757	-0.442704	0.083018	-0.107100	0.000786	0.880968	0.002928	0.180266
NVDA	0.001325	0.027282	-0.317969	0.093632	-0.187559	0.002067	-0.969943	-0.003244	0.417189
MSFT	0.001452	0.014360	-0.595245	0.075705	-0.054338	0.001475	0.182775	0.000609	0.219875
TWLO	0.002733	0.034306	-0.211827	0.354407	-0.137602	0.002354	-2.124550	-0.007133	NaN
AMZN	0.001170	0.018133	-0.486935	0.132164	-0.078197	0.001425	0.055118	0.000184	0.205338
GOOGL	0.000644	0.015159	-0.617195	0.096202	-0.075019	0.001013	-0.574688	-0.001919	0.105578
NFLX	0.001380	0.024406	-0.353175	0.135436	-0.102720	0.000590	-1.064248	-0.003560	0.209396
CRM	0.001084	0.017334	-0.514396	0.102713	-0.087061	0.001696	0.285698	0.000951	0.142038
DE	0.000880	0.016866	-0.540723	0.073045	-0.076512	0.001518	0.577800	0.001922	0.172470
SPLK	0.001542	0.025274	-0.334653	0.178932	-0.111918	0.001420	-0.269881	-0.000900	0.182443
VERI	-0.000402	0.065553	-0.158685	0.493011	-0.302837	-0.005348	1.190475	0.003953	NaN
SNPS	0.001067	0.014377	-0.621371	0.063589	-0.052558	0.001783	0.392652	0.001307	0.218864
QUIK	-0.000995	0.051383	-0.213989	0.375000	-0.307692	0.000000	3.386007	0.011162	-0.208536
BRN.AX	-0.000880	0.051811	-0.210003	0.386330	-0.191489	0.000000	4.761906	0.015628	-0.255373

table.sort_values(by='Average Return Yearly')

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Yearly	CAGR
TWLO	0.002733	0.034306	-0.211827	0.354407	-0.137602	0.002354	-2.124550	-0.007133	NaN
NFLX	0.001380	0.024406	-0.353175	0.135436	-0.102720	0.000590	-1.064248	-0.003560	0.209396
NVDA	0.001325	0.027282	-0.317969	0.093632	-0.187559	0.002067	-0.969943	-0.003244	0.417189
GOOGL	0.000644	0.015159	-0.617195	0.096202	-0.075019	0.001013	-0.574688	-0.001919	0.105578
SPLK	0.001542	0.025274	-0.334653	0.178932	-0.111918	0.001420	-0.269881	-0.000900	0.182443
AMZN	0.001170	0.018133	-0.486935	0.132164	-0.078197	0.001425	0.055118	0.000184	0.205338
MSFT	0.001452	0.014360	-0.595245	0.075705	-0.054338	0.001475	0.182775	0.000609	0.219875
CRM	0.001084	0.017334	-0.514396	0.102713	-0.087061	0.001696	0.285698	0.000951	0.142038
SNPS	0.001067	0.014377	-0.621371	0.063589	-0.052558	0.001783	0.392652	0.001307	0.218864
DE	0.000880	0.016866	-0.540723	0.073045	-0.076512	0.001518	0.577800	0.001922	0.172470
TCEHY	0.000811	0.020757	-0.442704	0.083018	-0.107100	0.000786	0.880968	0.002928	0.180266
VERI	-0.000402	0.065553	-0.158685	0.493011	-0.302837	-0.005348	1.190475	0.003953	NaN
QUIK	-0.000995	0.051383	-0.213989	0.375000	-0.307692	0.000000	3.386007	0.011162	-0.208536
BRN.AX	-0.000880	0.051811	-0.210003	0.386330	-0.191489	0.000000	4.761906	0.015628	-0.255373